CN111213210A - HLA tissue matching and methods therefor - Google Patents

Abstract

Contemplated systems and methods use high accuracy computer HLA analysis in order to build a transplant match database suitable for transplantation, particularly stem cell and solid organ transplantation.

Description

HLA tissue matching and methods therefor

This application claims priority to U.S. provisional application serial No. 62/554,655 filed on 6/9/2017.

Technical Field

The field of the invention is systems and methods for pre-transplant tissue matching, and more particularly to computer HLA determination.

Background

The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

HLA typing remains critical in the practice of transplanting various solid organs and stem cells, and there are various systems and methods known in the art to determine HLA typing in patients. Most commonly, HLA typing is performed using wet chemistry/serology methods or via nucleic acid analysis, in particular sequencing or PCR-based methods. These methods are satisfactory in many cases and will provide relatively accurate results. However, the most common methods will require a significant amount of time and are often relatively expensive, especially if a large population is to be analyzed.

To address at least some of the disadvantages associated with conventional methods, nested/tandem PCR, which typically uses a raw blood sample, can be employed as described in US 2011/0117553. In other methods suitable for high throughput assays, as taught in US2003/0165884, a combination of amplification and locus-specific capture probes is employed. Similarly, US7917297 describes arrays of various capture nucleotides on a solid phase to enable rapid analysis. Unfortunately, such systems are often not capable of highly accurate HLA determination, as the hybridization differences between HLA alleles are often only very small. All publications and patent applications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

More recently, computer-implemented methods have been described that use, for example, exome sequencing data to determine HLA typing, as discussed in US 2016/0125128. In other examples, probabilistic analysis of sequence data is employed to determine the most likely HLA type, as taught in US 2015/0110754, and WO2017/035392 describes computer analysis using de bruuinin (DeBruijn) plots. Notably, these methods are relatively fast, but have not been adopted for larger sample populations.

Thus, while various systems and methods for HLA typing are known in the art, there remains a need to provide improved systems and embodiments for HLA typing, particularly in silico HLA typing.

Disclosure of Invention

The present subject matter provides devices, systems and methods in which HLA analysis is performed on a large number of samples to provide a bioinformatic database that facilitates identification of donor/recipient matches. For example, when bone marrow stem cell transplantation is required for liquid tumors, a complete and detailed HLA analysis of all bone marrow and cord blood donors is first established. This unique database can then be used as a bioinformatics universal engine for any recipient that requires bone marrow or cord blood donation. In another example, for solid organ transplantation, a complete a priori sequence analysis is performed on all recipients to be transplanted to determine HLA typing. Once the donor organ is available, the database can become a universal matching engine for the finest HLA levels (including minor and rare alleles). Thus, it should be understood that the systems and methods presented herein provide rapid matching of donors and recipients at a highly accurate and comprehensive level for a large number of donors and/or recipients.

In one aspect of the inventive subject matter, the inventors contemplate a method of matching donor tissue and recipient tissue comprising the steps of obtaining omics data for a plurality of donor samples, wherein each sample is an umbilical cord blood or bone marrow sample, and the further step of determining HLA typing for each donor sample using a computer algorithm to obtain a donor registry (donor registry). In yet another step, a donor registry is used to identify one of the donor samples (e.g., cord blood) as compatible with a recipient having a matching HLA-type.

Also, in another aspect of the inventive subject matter, the inventors contemplate a method of matching donor tissue and recipient tissue comprising the steps of obtaining omics data for a plurality of recipients, wherein each recipient is a solid organ recipient, and determining HLA-typing for each recipient using a computer algorithm to obtain a recipient registry. In yet another step, a donor registry is used to identify one of the recipients as compatible with a donor organ (e.g., lung, liver, heart, or skin) with a matching HLA-type.

With regard to omics data for contemplated methods, exome sequencing data, whole genome sequencing data, and/or RNA sequencing data are particularly preferred, and it is also preferred that HLA typing is determined to at least four digit depth. Although not limiting to the inventive subject matter, the computer algorithm uses a de-bruton diagram and a reference sequence. Most typically, the reference sequence comprises alleles of at least one HLA type having an allele frequency of at least 1%, at least 10 different alleles of at least one HLA type, and/or alleles of at least two different HLA types. Thus, suitable HLA types include one or more than one of HLA-A, HLA-B, HLA-C, HLA-DRB-1 and HLA-DQB-1.

Various objects, features, aspects and advantages of the present subject matter will become more apparent from the following detailed description of preferred embodiments along with the accompanying figures in which like numerals represent like parts.

Drawings

Figure 1 is a table listing predicted and published HLA results for patient data from a public data set (1000 genome project; subjects NA19238, NA19239 and NA 19240).

Figure 2 is a table listing the computer predicted and laboratory validated HLA results for actual patient data.

Figure 3 is a table listing the in silico predicted HLA results obtained by performing long read sequencing (long read sequencing) of actual patient data.

Figure 4 is a table listing the computer predictions and HLA results obtained by performing long read length sequencing of actual patient data.

Detailed Description

The present inventors contemplate that a comprehensive and in-depth bioinformatic universal HLA matching engine/database can be built in a conceptually simple and fast method that requires only omics data from donor tissue or from recipients awaiting transplantation. Indeed, it should be noted that omic data can be obtained from an omic database or source intended for purposes other than HLA matching (e.g., for determining the likelihood of developing a disease, or family/ancestry determination), and such database and source can use the omic data to generate other values for the omic data provider. From a different perspective, it should be understood that the HLA type of the donor or recipient will be well established prior to the transplantation event. Thus, even individuals who inadvertently donate cells or organs when omics data is acquired can now be identified and linked as potential donors or recipients.

Thus, the inventors generally contemplate the use of various omics data to generate HLA libraries suitable as a universal data center for transplant donors and recipients. In this case, it must be recognized that omics data are currently customized and obtained for various reasons (for medical and other reasons). This increasing amount of omics information can now be used as a broad spectrum source of HLA information. For example, the histological data is customized or generated for use in determining ancestry or ethnicity, for health assessment (e.g., predicting risk of genetically related diseases), for identifying and/or monitoring specific populations, such as perpetrators, prison criminals, for population/ethnicity analysis in an epidemiological context, and for use in the course of personalized therapy (e.g., cancer immunotherapy).

Thus, it should be noted that the types of omics data vary widely and include whole genome sequencing, exome sequencing, transcriptome sequencing, and targeted sequencing. In this context, it should be recognized that current sequencing is almost exclusively aimed at specific target driven (e.g., identifying somatic or germline mutations, diagnosing disease, determining ethnicity, etc.). Contemplated systems and methods will advantageously allow the re-tailoring of the use of omics data to identify HLA-type, which may be beneficial to an individual providing omics data and/or to another individual having an HLA-match to the individual. Of course, it is noted that omics analysis may also be limited to groups of individuals that are intended a priori to be cell or tissue donors and/or cell or tissue recipients. Thus, exemplary donors include bone marrow or stem cell donors, platelet donors, organ donors, while exemplary recipients include acute transplant recipients and individuals with an increased likelihood of organ insufficiency or failure (e.g., due to a chronic progressive disease) or who are expected to require stem cell transplantation (e.g., after bone marrow ablation).

There are many sources of omics data known in the art, and all known sources are considered suitable for use herein. For example, contemplated omics data specifically include whole genome, exome sequencing and/or transcriptome sequencing data from healthy or diseased tissue. In other aspects of the inventive subject matter, only partial omics data may be obtained. In other options, such partial data includes data limited to chromosome 6, in particular position 6p 21.3. Thus, it should be noted that computer analysis of omics data can be very flexible and in fact obtain data from DNA and RNA omics analysis (e.g., RNA sequencing data, exome sequencing, whole genome sequencing) or a combination of DNA and RNA for HLA prediction. Furthermore, computer analysis as set forth in more detail below is highly accurate and very rapid, with run times typically less than 5 minutes, to obtain predictions for all 26 HLA types. Still further, new HLA alleles can be readily added to HLA reference groups for prediction, as will also be explained in more detail below. Finally, it should be appreciated that contemplated systems and methods generally do not require population-based heuristics to produce accurate results.

With respect to determining the HLA type of a potential transplant donor, it should be noted that when testing a donor, the determination can be made with or without the donor's intent to donate tissue to a third party with HLA matching. Some individuals may always wish to be available as donors, while others may consider such availability only months or even years after the initial determination. For example, some donors may store umbilical cord blood tissue of their children for potential use in regenerative medicine for the child, and the child may determine at certain times that their tissue (or HLA-typing information) may be used to help match recipients with the same or compatible HLA-typing. In another example, an individual will apply for a sequencing service that determines the genome, exome, and/or transcriptome of the individual for purposes other than HLA determination (e.g., paternity analysis, SNP analysis, disease risk propensity, family planning, personalized medicine, personalized health, personalized nutrition, etc.). For example, sequencing services (Otogenetics, Dante labs, 23Andme, throughput, MyHeritage, FamilyTreeDNA, etc.). Such a service may be provided as an additional incentive HLA determination to allow the user to be notified if one or more other individuals with matching or compatible HLA classifications are identified. Thus, particularly suitable sources of omics data include clinical services (i.e., for the purpose of treating a disease) and non-clinical commercial services (i.e., for purposes other than treating a disease) in which the genome, exome, and/or transcriptome is sequenced.

In other embodiments, blood or other organ banks may perform omics analysis on the tissue in inventory, possibly with the identification of the tissue donor. In this case, the blood or organ/tissue bank may also represent an HLA bank or HLA data source that may be contacted to determine one or more than one HLA match or HLA compatibility. Also, when a health care system (of a governmental or private nature) or insurance agency determines, stores and/or accesses omics data for a member or user, such histological data can be readily analyzed to determine HLA typing. Thus, it should be understood that an ever-increasing amount of integrated omics data can be used as a secondary source of HLA data that can be determined without medical procedures or the need to contact the individual for whom HLA typing is to be determined.

Similarly, with respect to determining the HLA type of a potential transplant recipient, it is contemplated that the potential recipient does not need to be transplanted urgently or even unexpectedly. Indeed, any person may develop a need for transplantation due to lifestyle, disease and/or treatment. For example, various lifestyle choices (e.g., drug use, excessive western diet, etc.) will be accompanied by an increased risk of organ failure, while diseases such as hepatitis, chronic kidney disease, diabetes, etc., will have an increased rate of progression of organ insufficiency/failure. On the other hand, certain cancer treatments (especially conventional chemotherapy) may result in organ damage, such as bone marrow dysfunction. In other instances, advances in regenerative medicine are expected to enable artificial organs from stem cells and/or progenitor cells. Since these cells are not usually removed from the recipient, HLA matching is crucial to avoid tissue rejection. Thus, while testing of the recipient is only required when a need for transplantation arises, HLA testing can be performed proactively. For example, HLA testing may be performed as an optional preemptive service, or upon visiting a doctor or clinic (often necessary due to signs and symptoms of disease). Most typically, such visits may be associated with conditions that can escalate to organ insufficiency or organ failure, or conditions that ultimately require transplantation. Similarly, the condition may require treatment to damage or kill organs or tissues, such as chemotherapy and/or bone marrow ablation.

Thus, it will be appreciated that contemplated methods for determining HLA type may be derived from a number of tissues (healthy or diseased), including inter alia, cord blood, whole blood, stem cells, buccal swabs, etc. Indeed, all donor tissues are considered suitable for use herein. Thus, suitable donor tissues include fresh liquid tissues (e.g., bone marrow aspirate, isolated stem cells), fresh solid tissues (e.g., skin tissue, cornea, kidney, lung, heart, etc.), and even preserved or cultured liquid tissues (e.g., frozen tissue sections, FFPE material, NK cells, T cells, optionally genetically engineered and/or cultured or cryopreserved). Furthermore, it is envisaged that HLA analysis need not be performed on donor tissue, but may also be performed on donors who agree to donate one or more tissues and/or organs when an HLA-compatible recipient is found or at the time of death. Thus, the matching database can be extended to also include potential donors.

For example, where bone marrow stem cell transplantation is required during treatment of a liquid tumor, the entire detailed HLA record of the bone marrow and/or cord blood donor in the HLA database can be queried. As described above, such a database may serve as a general engine for bioinformatics for any recipient who requires bone marrow or cord blood donation. In another example (e.g., in solid organ transplantation), a complete a priori sequence analysis may be performed on all recipients awaiting transplantation to determine the recipient's HLA type. This information may then be stored in a database. Once the donor organ is available, the database can be a universal matching engine for the finest HLA levels (including minor and rare alleles).

As will be readily appreciated, the HLA database may be information connected to a sequencing device, a sequence analysis device, a clinic, a (cord) blood bank, a (stem) cell bank, and/or a transplantation clinic, etc., or may be distributed over multiple computers. For example, the HLA database can be centrally located at a service center that queries the omics database to receive omics data or initiates remote omics analysis on a computer with the information connected to the omics database. Similarly, HLA analysis can also be performed in sequencing devices, sequence analysis devices, clinics, (cord) blood banks, (stem) cell banks, and/or transplantation clinics, and the results can be reported to the HLA database.

In this context, it should be noted that any language directed to a computer should be interpreted to include any suitable combination of computing devices, including servers, interfaces, systems, databases, agents, terminals, engines, controllers, or other types of computing devices operating alone or in combination. It should be appreciated that the computing device includes a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard disk drive, solid state drive, RAM, flash memory, ROM, etc.). The software instructions preferably configure the computing device to provide the actions, responsibilities, or other functionality discussed below with respect to the disclosed apparatus. In a particularly preferred embodiment, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPs, AES, public-private key exchanges, web services APIs, known financial transaction protocols, or other electronic information exchange methods. The data exchange is preferably performed over a packet-switched network, the internet, a LAN, a WAN, a VPN, or other type of packet-switched network.

For example, it is contemplated that omics data as described further below can be used to test tissue and organ donor samples for HLA typing, and that such testing can be performed at the same time as the sample or organ is collected. Alternatively, omics information can also be extracted from databases that have stored these data, especially where the donor has been genetically tested (e.g., whole genome sequencing, exome sequencing, etc.). Thus, it should be appreciated that the tissue or organ collection site may be different from the analysis site. For example, where the tissue is cord blood, bone marrow, or stem cells, omics testing can be performed directly on a portion of the tissue. On the other hand, when the organ is a solid organ, the test may be performed on a sample from the organ or on blood from a donor. On the other hand, when the recipient is awaiting tissue or organ transplantation, the recipient may be tested for HLA typing as described below, and HLA information may be stored in the database. Thus, it is noted that the sites of HLA testing/analysis and tissue or organ collection may be the same or different. Thus, HLA-match requests can be submitted from various sites, e.g., clinics, doctor's offices, laboratories, tumor collaboration groups, commercial sequencing entities, which can be physically or informally collocated with a sequencing center and/or HLA analysis services.

In a further contemplated aspect, HLA analysis can be provided as an ancillary service to a sequencing or omics processing center in order to provide an additional source of revenue. In this case, the HLA database may be a central registry that one or more parties may access based on certain credentials (e.g., organization membership, user level, access privileges, etc.). Furthermore, it is envisaged that such a central registry may use the entire genomic information of the recipient and/or donor tissue, or may use only limited omics information, typically sequence information associated with HLA location (chromosome 6p 21.3).

Thus, HLA matches can be identified at any one or more than one site, e.g., a sequencing device, a tumor collaboration group, a clinic, a physician's office, a sequence analysis device, a (cord) blood bank, a (stem) cell bank, a transplantation clinic, and/or an HLA database. HLA matching is generally considered a matched or correlated HLA typing in which at least one, or at least two, or at least three, or at least four, or at least five, or at least six of the HLA alleles have at least two, more typically at least four, and most typically at least six, identities. Typical examples of HLA alleles having HLA typing include one or more than one of HLA-A alleles, HLA-B alleles, HLA-C alleles, HLA-DRB-1 alleles and HLA-DQB-1 alleles, each of which has a specific type.

While HLA typing can be determined in a variety of ways, all or almost all of them require a significant amount of time and equipment. Furthermore, even when using allele-specific PCR reactions for targeted HLA determination, accuracy is often less than desirable due to the very small differences in base composition and melting point. Thus, many conventional HLA typing methods are unable to resolve HLA typing to more than two or four digits. Furthermore, conventional HLA typing methods are generally not practical for testing rare HLA types and therefore tend to limit the ability to match. Still further, conventional HLA testing is only performed for transplant recipients for an impending transplant. Likewise, HLA testing is typically performed on most donors where the donor has agreed or has considered tissue donation (to themselves or others). To address these difficulties, the inventors now contemplate creating HLA databases using omics data available from any individual regardless of the individual status (i.e., whether a particular individual is a donor or recipient, or whether the individual has considered or consented to a cell or organ donation). In this way, a universal HLA database can be created with a significantly larger range of donors and recipients.

Most advantageously, existing omics data, such as whole genome, exome, and/or transcriptome sequence data, will be processed in an analysis module where omics sequences from individuals are processed using a de bruton diagram-based approach in conjunction with synthetic reference sequences that include known sequence information for a large number of HLA allele sequences (e.g., HLA-a allele sequences, HLA-B allele sequences, HLA-C allele sequences, HLA-DRB-1 allele sequences, and HLA-DQB-1 allele sequences) in order to obtain a highly accurate alignment of the various closely related sequences. It will be appreciated that such an analysis is particularly advantageous for determining HLA based on DNA and/or RNA sequencing information, as each HLA type has many alleles which are typically very similar, as conventional alignment methods typically do not have significant discriminatory power when the sequences have a high degree of similarity.

Indeed, HLA allele identification is one of the most complex analytical problems in molecular diagnostics. First, it is now known that there are more than 1300 alleles at 12 expressed class I and class II loci in the world population. In addition, the encoded polypeptides of these alleles differ from each other by one or more amino acid substitutions, resulting in substantial polymorphism. For example, the HLA-B locus has over 400 known alleles. Second, new alleles are continuously added to known sequences, making standard protocols quickly obsolete. Third, clinical laboratories are often required to provide allele identification at various resolution levels for different clinical situations (e.g., non-related bone marrow transplantation requires high resolution allele level typing, while serological or low resolution typing is sufficient for kidney transplantation). All of these difficulties are confounded by the fact that individuals have alleles from both maternal and paternal sources, and that differences between alleles are usually only very minor (e.g., single, two, three changes in four amino acids). Table 1 below illustrates the broad diversity of HLA alleles.

TABLE 1

Thus, the error frequency of hybridization-based methods such as sequence-specific oligonucleotide probe hybridization or sequence-specific primer PCR is relatively high. Similarly, although direct sequencing of PCR products would eliminate the difficulties associated with hybridization, analysis of sequence reads is still time consuming, especially where large numbers of samples must be processed. In this context, it should be noted that the systems and methods presented herein improve overall speed and accuracy as well as computer functionality, as the construction and ordering of the de bruton's graphical elements (and weights) greatly improves accuracy and speed as compared to conventional data formats and processing schemes (e.g., multiple sequence alignment algorithms). Furthermore, it must be understood that the problems solved by the inventors are specific to the field of bioinformatics and even absent without computational information. Finally, it should be recognized that the tasks performed by the analysis engine cannot be reasonably performed in a person's lifetime without the aid of a computer system.

In a typical example, a relatively large number of patient sequence reads mapped to chromosome 6p21.3 (or any other location near/at which HLA alleles are found) are provided by an omic database (e.g., from a clinic, a tumor collaboration group, a commercial genome analysis company, etc.) or a sequencing device or machine. Most typically, sequence reads will be generated by NextGen sequencing (e.g., Illumina Solexa, Roche454 sequencer, Ion Torrent sequencer, etc.), have a length of about 100 to 300 bases, and contain metadata including read quality, alignment information, orientation, position, etc. Suitable formats include SAM, BAM, FASTA, GAR, etc., and it is generally preferred that the patient sequence reads provide a depth of coverage of at least 5x, more typically at least 10x, even more typically at least 20x, and most typically at least 30 x. In addition to patient sequence reads, contemplated methods also employ one or more reference sequences that include sequences of multiple known and distinct HLA alleles.

For example, a typical reference sequence can be a synthetic (without a corresponding human or other mammalian counterpart) sequence that includes at least one HLA-typed sequence fragment having multiple HLA alleles of the HLA type. For example, suitable reference sequences include a collection of known genomic sequences of at least 50 different alleles of HLA-a. Alternatively, or in addition, the reference sequence may also comprise a collection of known RNA sequences of at least 50 different alleles of HLA-a. Of course, as discussed in more detail below, the reference sequence is not limited to 50 alleles of HLA-a, but the reference sequence may have other compositions in terms of HLA typing and number/composition of alleles. HLA typing is typically expressed in a conventional fashion. For example, HLA typing of a particular HLA gene may be represented as HLA-a 24:02:01:02L, where the first letter represents the HLA gene, where 24:02 represents the type and subtype, where: 01 represents a synonymous substitution, and wherein 02 represents a substitution in a non-coding region. The last letter indicates protein expression. Suitable HLA allele sequences for synthetic reference include all known sequences and are available from IPD-IMGT/HLA (URL: ebi. ac. uk/IPD/IMGT/HLA /).

Most typically, the reference sequence will be in a computer readable format and will be provided from a database or other data storage device. For example, suitable reference sequence formats include FASTA, FASTQ, EMBL, GCG, or GenBank formats, and may be obtained or constructed directly from data in a common data repository (e.g., IMGT, international ImmunoGeneTics information system or allele frequency network database, eurostat, www.allelefrequencies.net). Alternatively, the reference sequence may be constructed from known HLA alleles of the individual based on one or more predetermined criteria, such as allele frequency, ethnic allele distribution, common or rare allele types, and the like.

Using the reference sequence, patient sequence reads can now be run through a de bruney plot to identify the best-fit allele as described in WO2017/035392 (and its U.S. national phase congener). In this context, it should be noted that each individual carries two alleles for each HLA type, and these alleles may be very similar, or in some cases even identical. This high degree of similarity poses a significant problem for conventional alignment schemes. The present inventors have now found that HLA alleles, and even very closely related alleles, can be resolved using a method in which a debbruton diagram is constructed by breaking down sequence reads into relatively small k-mers (typically 10 to 20 bases in length) and by performing a weighted voting process in which each patient sequence read votes for each allele as such (a "quantitative read support") based on the k-mers of the sequence reads that match the allele sequence. The cumulative highest vote for the allele then indicates the most likely predictive HLA allele. Furthermore, it is generally preferred that each segment that matches an allele is also used to calculate the overall coverage and depth of coverage of that allele, as shown in more detail below.

To identify the second alleles of the same HLA type, the inventors found that even relatively similar second alleles can be addressed in a more heuristic approach, where the highest-ranking HLA alleles are removed from further consideration, and where the remaining alleles are reordered using adjusted ("scaled") voting results. More specifically, the reordering is performed to reduce the number of votes for k-mers matching the highest ranked allele in the reordered voting results. Such adjusted voting results reduce (but not eliminate) the weighted voting results for genotypes similar to the highest-ranked alleles, and thus reduce the weight of genetically related alleles. At the same time, similar alleles are not ignored. The ordering can be further refined by considering the total coverage and the depth of coverage. For example, a first reordered allele may score higher with overall coverage and depth of coverage significantly lower than a second reordered allele. In this case, the second reordered allele is more likely to be the correct allele. The highest ranked re-ranked allele is then the second allele of the same HLA-type. Of course, as described above, reordering can factor in overall coverage and depth of coverage, and can even result in disqualification of alleles where overall coverage and/or depth of coverage drops below a user-defined threshold (e.g., overall coverage is less than 94%, and/or depth of coverage is less than 10 x). Furthermore, the use of matching k-mers as votes also allows unique k-mers to be identified in a particular voting result, which may serve as further guidance whether or not a particular voting result is likely to be a correct prediction.

Of course, it should be appreciated that analysis and HLA prediction need not be limited to a particular HLA type, but rather, all HLA types and allelic variants contemplated herein, including HLA-E, HLA-F, HLA-G, HLA-H, HLA-J, HLA-K, HLA-L, HLA-V, HLA-DQA1, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DRA, HLA-DRB345, HLA-MICA, HLA-MICB, HLA-TAP1, HLA-TAP2, and even newly discovered HLA types and their corresponding alleles. Furthermore, it will be appreciated that the analysis need not be limited to a single HLA type, but is applicable to multiple HLA types herein. Thus, a reference sequence may comprise two, three, four or more than four HLA-types, as well as a set of alleles for the respective HLA-types. Since each HLA type has a significant number of alleles, it is envisaged that not all known alleles need to be included in the reference sequence. For example, a reference sequence can include alleles having an allele frequency above a particular threshold, e.g., an allele frequency of at least 0.1%, or at least 0.5%, or at least 1%, or at least 2%, or at least 5%. Thus, from a different perspective, suitable reference sequences may comprise at least 10, or at least 30, or at least 50, or at least 100, or at least 200 or at least 500, or even more different alleles of at least one HLA-type.

Similarly, it should be appreciated that the nature and type of patient sequence reads can vary significantly. For example, contemplated patient sequence reads include DNA and RNA sequences, each of which can be obtained using all methods known in the art. Moreover, such sequence reads may be provided by a data storage system (e.g., a database) or by a sequencing device. For example, DNA sequence reads may be derived from an NGS sequencer and RNA sequences may be from a rtPCR sequencing device. Thus, the length of a patient sequence read will typically be longer than 20 bases, more typically longer than 50 bases, and most typically longer than 100 bases, however, typically shorter than 5000 bases, or less than 3000 bases, or shorter than 1000 bases. Thus, contemplated patient sequence reads may have a length of 100 to 500 bases or 150 to 1000 bases.

To reduce computation time and the need for data storage systems and/or memory, it is also preferred that patient sequence reads be pre-selected to the genomic region where the HLA-typing gene is located. For example, patient sequence reads mapping to chromosome 6p21.3 are particularly contemplated. Similarly, patient sequence reads may also be selected based on one or more annotations indicating possible locations of known HLA alleles in the genome. Alternatively, the annotation may also refer directly to the possibility of the sequence being an HLA allele.

Regardless of the length of the patient sequence reads, it is generally preferred to decompose the patient sequence reads with k-mers having relatively short lengths, and particularly preferred lengths are generally 10 to 30. Notably, such short k-mer lengths allow for greater resolution and accuracy in variant identification, particularly due to weighted voting on fragments containing such k-mers. Thus, the length of the k-mer is typically 10 to 30, alternatively 15 to 35, alternatively 20 to 40. From a different perspective, the length of the k-mer is preferably less than 60, even more preferably less than 50, and most preferably less than 40, but longer than 5, more typically longer than 8, and most typically longer than 10. For example, a suitable k-mer would therefore be 5% to 15% of the length of the patient sequence read.

With respect to ranking and composite match scores, it should be noted that in a most preferred aspect, match scores are generated based on all k-mers present in the patient sequence reads, and each voted (i.e., matched) k-mer has the same voting weight. As a result, the patient sequence reads will have a specific quantitative read support for each allele in the reference sequence. Furthermore, since in most cases each location in the genome has a sequencing depth >1, and since each patient sequence read will cover only a portion of the full length of the allele, each allele can obtain multiple tickets from multiple patient sequence reads. Most typically, all the tickets for an allele are added to get a composite match score for that allele. The composite match score for each allele is then used for ranking and further analysis.

However, in alternative aspects of the inventive subject matter, it should be noted that the scoring and calculation of composite scores may also be modified to achieve one or more specific goals. For example, the match score for a fragment need not be calculated from all matching k-mers, but only a selection of random numbers or k-mers may be counted. On the other hand, k-mers with less than perfect matches (e.g., 14/15 matches) may be given voting weights that may have lower voting weights. Likewise, and particularly where metadata is available, the voting weight of k-mers and/or patient sequence reads in which the read quality falls below a particular threshold can be reduced. On the other hand, in the presence of a lower sequencing depth, a particular fragment may represent an excessive number of votes. In yet another contemplated aspect, particularly where the read depth is relatively high (e.g., at least 15x, or at least 20x, or at least 30x), co-located patient sequence reads may be eliminated or included based on the number of votes. Thus, the composite match score may be based on all available tickets, or only a portion of the tickets available for the allele.

Although the ranking is typically dependent on the cumulative match score, it should be appreciated that at least one factor may also be used to correct the ranking. These correction factors include coverage fraction (fraction coverage), sequencing depth, number of unique k-mers, and metadata of available fragments. For example, the voting weight may be reduced for alleles where the coverage of the allele is below a predetermined threshold (e.g., less than 96%, or less than 94%, or less than 92%, etc.) and/or where the sequencing depth is below a predetermined threshold (e.g., less than 15x, or less than 12x, or less than 10x, etc.). On the other hand, for example, for alleles where the percentage of unique k-mers is above a predetermined threshold (e.g., above 2%, or above 5%, or above 10%), the voting weight may also be increased.

The highest ranked allele is typically the first predicted allele for a given HLA-type, while the second ranked allele may be the second allele for the same HLA-type. It should be noted, however, that the scoring process can be further refined or refined as desired, especially where many of the top ranked rankings have similar composite match scores (e.g., a significant portion of their scores are from a highly shared set of k-mers). In a preferred example, a score refinement procedure may be implemented that includes a re-calculation in which the weight of k-mers that match (perfectly match, or have at least 90%, or at least 95%, or at least 97%, or at least 99% similarity) the highest ranked k-mers may be reduced by a correction factor. Such a correction factor may reduce the number of votes by any predetermined amount. The most common correction factors reduce the number of tickets by 10%, or 20% to 40%, or 40% to 60%, or even more than 60%. This has the effect of reducing the weighted voting of genotypes similar to the highest ranked alleles, making different genotypes relatively more important. Thus, it should be noted that the first allele is identified based on the highest support from all sequencing data, while the second allele is identified based on a more heuristic approach that uses both of raw weighted voting, proportional weighted voting, and coverage to determine whether the second allele has support in the dataset (e.g., high proportional weighted voting and genotype coverage) or whether the first genotype of the genome is homozygous (e.g., high initial weighted voting, very low proportional weighted voting, no other alleles with appropriate coverage). From a different perspective, even if there is an allele that is similar to the highest ranked allele, reordering is beneficial to more accurately distinguish the second allele. Furthermore, this method also allows for easy identification of homozygous HLA-types. Furthermore, it will be appreciated that this approach does not require the use of a hash table and can allow identification of suitable HLA alleles without assembly of sequence reads into HLA typing. Still further, contemplated systems and methods also allow for the use of DNA and/or RNA data.

It will therefore be appreciated that the above described methods and systems are particularly suitable for large scale HLA determination from a variety of omics data, where presence or omics data can be used to analyze more than 100 individuals, or more than 200 individuals, or more than 500 individuals, or more than 1000 individuals, or more than 5000 individuals, or more than 10000 individuals, or even more than 10000 individuals. At the end of the analysis, each individual's HLA type is stored in an HLA database that is accessible by multiple parties, including the parties that provide or make available omic data and third parties interested in finding HLA-compatible or identical records or individuals. Such HLA-compatible or identical records or individuals may be used for various purposes. First, HLA matching will be used for cell or organ transplantation, but also for determining familial relationships, determining ethnicity, determining the identity of blood or tissue samples (e.g., in forensic use), and the like.

Examples

To validate HLA prediction, three independent known patient records and samples were obtained from the 1000 genome project (NA19238, NA19239 and NA19240) and HLA typing was then predicted as described above. Notably and unexpectedly, HLA determination and prediction using the de bruuene diagram method described above has a near perfect match, in addition to HLA-C (for NA19238), DRB1 (for NA19239), and HLA-C (for NA19240), as shown in fig. 1. Notably, these three inconsistencies may be interpreted as due to incorrect data in the publication record. The HLA prediction methods presented herein have demonstrated 100% accuracy in different groups of 5 HLA in 3 independent data sets. From this data, the "public" C18: 01 could not be supported, while the predicted C18: 02 was fully supported. Furthermore, mendelian inheritance determined that "published" DRB1 x 13:01 on both alleles was not possible (provided NA19238 and NA19239 are the parents of NA 19240).

In a further experiment, the inventors predicted HLA-A, HLA-B, HLA-C, HLA-DRB and HLA-DBQ haplotypes for 20 actual patient samples and verified the predicted HLA typing in a contracting laboratory using Sequence Specific Oligonucleotide (SSO) and Sequence Specific Primer (SSP) methods. As can be seen from fig. 2, the predicted accuracy was 100% for all 20 patient samples. Likewise, another 40 patients were analyzed and the predicted HLA typing was verified using long range sequencing (PacBioSMRT sequencing). It is worth noting that as can be seen from fig. 3and 4, only 4 predictions are inconsistent, and 7 predictions are uncertain because the sequence cannot be determined. The remaining 97.4% of all data was consistent with predicted HLA typing.

As will be readily appreciated, the predicted HLA-typing may be stored in a database and may represent donor HLA-typing, particularly bone marrow donors, stem cell donors, cord blood donors, and the like, and/or transplant recipients, such as patients awaiting heart, liver, lung, kidney, skin, or pancreas transplantation.

As used herein, and unless the context indicates otherwise, the term "connected to" is intended to include both direct connections (where two elements that are connected to each other are in contact with each other) and indirect connections (where at least one additional element is located between the two elements). Thus, the terms "connected to" and "connected to" are used synonymously. Furthermore, the grouping of alternative elements or embodiments of the invention disclosed herein is not to be construed as limiting. Each group member may be referred to and protected individually or in conjunction with other members of the group or other elements found herein. One or more members of a group may be included in or deleted from the group for convenience and/or patentability reasons. When any such inclusion or deletion occurs, the specification is considered to contain the modified group, thereby enabling the written description of all markush groups used in the appended claims.

It will be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the scope of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprises" and "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. When the specification and claims refer to at least one selected from A, B, C … … and N, the text should be interpreted as requiring only one element from the group, rather than a plus N, or B plus N, etc.

Claims (20)

1. A method of matching donor tissue with recipient tissue, comprising:

obtaining omics data for a plurality of donor tissue samples, wherein each donor tissue sample is an umbilical cord blood or bone marrow sample;

determining HLA type for each donor tissue sample from omics data using a computer algorithm;

generating a donor registry storing respective HLA-types for a plurality of donor tissue samples; and

identifying one of the donor tissue samples as compatible with a recipient having a matching HLA-type in the donor registry.

2. The method of claim 1, wherein the omics data are exome sequencing data, whole genome sequencing data, and/or RNA sequencing data.

3. The method of claim 1, wherein the HLA typing is determined to at least four digits deep.

4. The method of claim 1, wherein the computer algorithm uses a de brunette diagram and a reference sequence comprising a plurality of alleles of at least one known HLA-type.

5. The method of claim 4, wherein the reference sequence comprises an allele of at least one HLA type, the allele having an allele frequency of at least 1%.

6. The method of claim 4, wherein the reference sequence comprises at least ten different alleles of at least one HLA type.

7. The method of claim 4, wherein the reference sequence comprises alleles of at least two different HLA types.

8. The method of claim 1, wherein the HLA-typing is at least three of HLA-a typing, HLA-B typing, HLA-C typing, HLA-DRB-1 typing and HLA-DQB-1 typing.

9. The method of claim 1, wherein the matching HLA-type is determined by serotyping.

10. The method of claim 1, wherein the donor sample is cord blood.

11. A method of matching donor tissue with recipient tissue, comprising:

obtaining omics data for a plurality of transplant recipients, wherein each transplant recipient is a recipient for a solid organ;

determining HLA type for each transplant recipient from omics data using a computer algorithm;

generating a recipient registry storing respective HLA classifications for a plurality of transplant recipients; and

identifying in the recipient registry one of the recipients as compatible with a donor organ having a matching HLA-type.

12. The method of claim 11, wherein the omics data are exome sequencing data, whole genome sequencing data, and/or RNA sequencing data.

13. The method of claim 11, wherein the HLA typing is determined to be at least four digits deep.

14. The method of claim 11, wherein the computer algorithm uses a de-bruton diagram and a reference sequence.

15. The method of claim 14, wherein the reference sequence comprises an allele of at least one HLA-type, the allele having an allele frequency of at least 1%.

16. The method of claim 14, wherein the reference sequence comprises at least ten different alleles of at least one HLA-type.

17. The method of claim 14, wherein the reference sequence comprises alleles of at least two different HLA types.

18. The method of claim 11, wherein the HLA-type is HLA-a type, HLA-B type, HLA-C type, HLA-DRB-1 type, or HLA-DQB-1 type.

19. The method of claim 11, wherein the HLA-typing is at least three of HLA-a typing, HLA-B typing, HLA-C typing, HLA-DRB-1 typing and HLA-DQB-1 typing.

20. The method of claim 11, wherein the donor organ is a lung, liver, heart, or skin.

Images